Paul R. MoorcroftDavid Medvigy, Stephen Wofsy, J. William Munger, M. Dietze

Harvard University

Developing a predictive science of the biosphere

- we now have models that make predictions for the long-term responses of terrestrial ecosystems to climate change.

- but are they predictive?

carbon flux: land-air global mean temperature

- existing ‘big-leaf’ dynamic terrestrial biosphere models (DGVMs) are interesting, but largely unconstrained hypotheses for the effects of climate variability and change on terrestrial ecosystems.

- models are fundamental to inference about the state of carbon cycle because the predictions of interest are at scales larger than those at which most measurements are made.

atm

osph

eric

C

O2 m

eas.

satellite observations(leaf phenology, soil

moisture)

Can

opy

CO

2 &

H2O

flux

es.

forest inventories (vegetation dynamics)

spatial scale 1m2

1000km2

100km2

10km2

1km2 earth

decades

years

months

hours

time

scal

e

- as a result, scaling is a key issue

(Moorcroft 2006)

Aircraft measurementsof CO2 & H2O fluxes

Ecosystem Demography Model (ED2)

ha (~10-2 km2)

(Moorcroft et al. 2001,Medvigy et al. 2006)

of plant type i

mortality growth

waternitrogencarbon

recruitment

~ 15 m

leaf carbon fluxes

evapo-transpiration

carb

on

up

take

(N

EE

tC h

a-1

y-1)

Harvard Forest LTER ecosystem measurements

- initialize with observed stand structure

- model forced with climatology and radiation observed at Harvard Forest meteorological station.

ED2 biosphere model

Atmospheric Grid Cell

ED-2 model fitting at Harvard Forest (42oN, -72oW)

- 2 year model fit (1995 & 1996), in which model was constrained against:

- hourly, monthly and yearly GPP and Rtotal - hourly ET - above-ground growth & mortality of deciduous & coniferous trees

optimizedinitialobserved

= optimization period

Improved predictability at Harvard Forest: 10-yr simulations (1992-2001)

Net Carbon Fluxes (NEP)

Improved predictability at Harvard Forest: 10-yr patterns of tree growth and mortality (1992-2001)

observedinitial optimized

growth

mortality

= optimization period

GPP

respiration (ra + rh )

Improved predictability at Harvard Forest: 10-yr simulations (1992-2001)

observedinitial optimized = optimization period

conifers hardwoods

mor

tali

tygr

owth

Vegetation model optimization: results

model parameters are generally well-constrained: average coefficient of variation: 17%

(= 95% confidence interval)

(-85,

+160)

Change in goodness of fit: 450 log-likelihood (l) units (sig level: l= 20)

HowlandForest

Harvard Forest

Howland Forest (45oN, -68o W)

Howland forest Composition:

growth

observedinitial optimized

net carbon fluxes (NEP)

(no changes in any of the model parameters)

Gross Primary Productivity (tC ha-1 mo-1 )

conifer basal area increment (tC ha-1 mo-1 )

hardwood basal area increment (tC ha-1 mo-1 )

Improved predictability at Howland Forest: 5-yr simulations (1996-2000)

=> model improvement is general, not site-specific

Regional Simulations

- climate drivers : ECMWF reanalysis dataset

- stand composition & harvesting rates: US Forest Service & Quebec

forest inventory 1985 - 1995

- again, no change in any of the model parameters

Harvard Forest

initial

Regional decadal-scale dynamics of above-ground biomass growth (tC/ha/yr)

observed optimized

Conclusions: Developing a predictive science of the biosphere

structured biosphere models such as ED2 can be parameterized & tested against field measurements yielding a model with accurate:• canopy-scale carbon & water fluxes • tree-level growth & mortality dynamics (the processes that govern long-term vegetation change)

capture observed regional scale variation in ecosystem dynamics without the need for site-specific parameters or tuning (scale accurately in space).

capture short-term & long-term vegetation dynamics(scale accurately in time).

Able to demonstrate that:

shown that it is possible to develop terrestrial biosphere models that not only make predictions about the future of ecosystems but are also truly predictive.

optimization site

Ameriflux site

Future Directions:

North American Carbon Plan (NACP): expanding to sub-continental scale.

Biosphere-atmosphere feedbacks Amazonia

(Cox et al 2000)

Amazonian deforestation predicted to change South American climate

(Shukla et al 1990)

Change in Annual Precipitation (mm)

Santarem Flux tower

(3oS, -55oW)

Forest Inventory:

Predicted collapse of the Amazon forests in response to rising CO2

Collaborators: Steve Wofsy, Bill Munger, Roni Avissar, Bob Walko, D. Hollinger, Andrew Richardson

Lab: Marco Albani, David Medvigy, Daniel Lipsitt, M. Dietze

Acknowledgements

References:Moorcroft et al. 2001. Ecological Monographs 74:557-586. Hurtt et al. 2002. PNAS 99:1389-1394.Albani & Moorcroft (2006) Global Change Biology 12:2370-2390Moorcroft (2006) Trends in Ecology and Evolution 21:400-407Medvigy et al. (2007) Global Change Biology (in review)

Funding: National Science Foundation Department of EnergyNational Aeronautics and Space Administration

Soil decomposition model

temperature sensitivity f(T) soil moisture sensitivity f()

rela

tive

de

com

posi

tion

ra

te

optimized

initial

3-box biogeochemistry model (fast, structural & slow C pools)